CN108009366A - Geostationary Satellite's based on MLE and Monte-Carlo Simulation - Google Patents
Geostationary Satellite's based on MLE and Monte-Carlo Simulation Download PDFInfo
- Publication number
- CN108009366A CN108009366A CN201711291915.2A CN201711291915A CN108009366A CN 108009366 A CN108009366 A CN 108009366A CN 201711291915 A CN201711291915 A CN 201711291915A CN 108009366 A CN108009366 A CN 108009366A
- Authority
- CN
- China
- Prior art keywords
- failure
- satellite
- random
- product
- reliability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides the Geostationary Satellite's based on MLE and Monte-Carlo Simulation.Satellite failure is divided into two major class of random failure and wearout failure by this method, establish the life model of random failure product, according to each unit crash rate of satellite random failure product and its reliability logic relation, using monte carlo simulation methodology, the mean time to failure MTTF of satellite system random failure is tried to achieve, is derived from satellite system random failure reliability function RAt random(t)；Establish the life model of wearout failure product, according to each unit reliability logic relation of satellite wearout failure product and its wear-out life average and variance, utilize monte carlo simulation methodology, the average and variance evaluation of satellite wear-out life is calculated, is derived from satellite system wearout failure reliability function RConsume(t)；Satellite system random failure and wearout failure function are subjected to integration analysis, satellite mean mission duration time, MMDT MMD is obtained, in this, as the life prediction result of satellite system.This method calculates accurate objective.
Description
Technical field
The present invention relates to based on MLE (MLE:Mean Lifetime Estimate average life spans estimate) and Monte Carlo imitate
The Geostationary Satellite's of genuine Geostationary Satellite's and Monte-Carlo Simulation, belong to reliability assessment technical field.
Background technology
Life-span prediction method has the Predicting Technique method based on model, Predicting Technique method based on probability and based on data
Drive Predicting Technique method.General satellite system is directed to the multi-field subject such as electronics, heat, power, electromagnetism, and in-orbit environment is more in addition
Become, is severe, therefore the physical-chemical structure for building satellite system seems more arduous；Simultaneously for aerospace industry, the big rule of satellite
Mould production is certainly unrealistic, and the statistical analysis of satellite failure and reliability data all suffer from sample rareness, probability statistics face
It is the difficult selection for calculating the reliability that accurate " average " still obtains uncertain " specific " satellite to face.
The content of the invention
The technology of the present invention solves the problems, such as：Overcome the deficiencies in the prior art, proposes based on average life span to estimate and cover special
The Geostationary Satellite's of Caro emulation, realize the prediction to satellite mean mission duration time, MMDT (MMD).
The present invention technical solution be：Geostationary Satellite's based on MLE and Monte-Carlo Simulation, this method
Include the following steps：
(1), criterion is terminated according to the lifetime of satellite, determines each subsystem units product that the lifetime of satellite can be caused to terminate；
(2), each subsystem units product that the lifetime of satellite can be caused to terminate determined by step (1) is divided into random mistake
Imitate two class of product and wearout failure product；
(3), the life model of random failure product is established, according to each unit crash rate of satellite random failure product and Qi Ke
By property logical relation, using monte carlo simulation methodology, the mean time to failure of satellite system random failure is tried to achieve
MTTF, is derived from satellite system random failure reliability function RAt random(t)；
(4), the life model of wearout failure product is established, is closed according to each unit reliability logic of satellite wearout failure product
System and its wear-out life average and variance, using monte carlo simulation methodology, are calculated average and the side of satellite wear-out life
Difference estimation, is derived from satellite system wearout failure reliability function RConsume(t)；
(5), satellite system random failure and wearout failure function are subjected to integration analysis, obtain the satellite task that is averaged and continue
Time MMD, in this, as the life prediction result of satellite system.
The life model of the random failure product is the reliability model of the product.
Step (3) are implemented as：
(3.1), simulation parameter is initialized, the simulation parameter includes：Simulation times thresholding N, default emulation step number threshold value
K, analysis time t, analysis time step delta t, emulation step number i；
(3.2), emulation step number i is added 1, renewal emulation step number i；
(3.3), initialize simulation times n and meet that the reliability number r of reliability requirement is 0；
(3.4), simulation times n is added 1；
(3.5), random number is produced as random Reliability index by the use of the random function of MATLAB, produced according to random failure
The life model of product, calculates the failure reliability index corresponding out-of-service time of each random failure product unit, with reference to
Reliability logic relation between machine failure each unit of product, obtains the service life T of random failure products, enter step (3.6)；
(3.6), the service life T of random failure product is comparedsThe index of aging T given with satellitemSize, if random lose
Imitate the service life T of productsThe index of aging T given more than or equal to satellitem, represent that random Reliability index at this time meets the system longevity
Index request is ordered, the reliability number r for meeting reliability requirement is added 1, reliability number r is updated, otherwise, as simulation times n
During not up to simulation times thresholding N, repeat step (3.4)~step (3.6), otherwise, enters step (37)；
(3.7), analysis time t corresponding satellite system reliability is calculatedAnalysis time t is according to simulation step length Δ
T is incremented by, will analysis time t plus Δ t with replacement analysis time t；
(3.8), emulation step number is added 1, renewal emulation step number i, compares emulation step number and be less than or equal to default emulation step number
Threshold k, then repeat step (3.2)~step (3.8), obtains the longevity of the random failure product corresponding to each simulation analysis time
Order Ts, otherwise, enter step (3.9)；
(3.9), each simulation analysis time t+k Δ t, the service life T of corresponding random failure products, make satellite system
System reliability curve, is fitted curve to obtain satellite system random failure function RAt random(t)。
Judged in step (3.5) by the reliability logic relation between each unit of random failure product, obtain with
The service life T of machine failure productsSpecific method be：
When the reliability logic relation between each unit of random failure product is parallel relationship, put down before choosing first-time fault
The greater of equal time MTTF, the service life T as random failure products；
When the reliability logic relation between each unit of random failure product is series relationship, put down before choosing first-time fault
The smaller of equal time MTTF, the service life T as random failure products；
When the reliability logic relation between each unit of random failure product is voting relation, put down before choosing first-time fault
The summation of equal time MTTF, the service life T as random failure products。
The life model of the wearout failure product uses normal distribution form, is specially：
Wherein：
μLoss- satellite wear-out life average；
σLoss- satellite wear-out life variance.
Step (4) are implemented as：
(4.1), wearout failure product simulation parameter is initialized, the wearout failure product simulation parameter includes simulation times
Thresholding N2；
(4.2), the lifetime data of collection consume class unit, calculates first moment, second moment obtains wearout failure attrition
Service life average and variance, for there are the wearout failure product of redundancy backup, its wear-out life is calculated using Monte-Carlo Simulation
Average and variance；
(4.3), according to wearout failure attrition service life average and variance, the life model of wearout failure product is established,
Carry out N2Secondary Monte-Carlo Simulation calculates, and obtains the wearout failure time simulation value of each random failure product；
(4.4), according to the wearout failure time simulation result of wearout failure product, satellite average loss service life average is obtained
μLossAnd variances sigmaLossEstimation so that obtain satellite wearout failure average life span estimation, also therefore obtain wearout failure
Reliability function RConsume(t)。
The calculation formula of step (5) the satellite mean mission duration time, MMDT MMD is：
In formula, T is truncated time.
The truncated time T chooses 1.5 times of satellite design lifetime requirement.
Compared with the prior art, the invention has the advantages that：
(1), satellite failure is divided into random failure and wearout failure by the present invention, by monte carlo simulation methodology, is realized
Life prediction to satellite mean mission duration time, MMDT.After satellier injection, status information, the failure of satellite are obtained by remote measurement
Information, can carry out life prediction according to the new state of satellite near real-time, compared to existing with satellite time or satellite in orbit
The result more science that projected life is predicted as the lifetime of satellite, it is also more objective to calculate.
(2), the present invention has fully ensured that the convergence of process, so as to also ensure that Forecasting Methodology by simulation means
Precision.
Brief description of the drawings
Fig. 1 is the Geostationary Satellite's flow chart of the invention based on MLE and Monte-Carlo Simulation；
Fig. 2 is the Monte-Carlo Simulation process flow diagram flow chart of random failure product of the embodiment of the present invention；
Fig. 3 is satellite average loss life estimation method (MLE) of the embodiment of the present invention based on Monte-Carlo Simulation；
Fig. 4 is satellite mean mission duration time, MMDT (MMD) Definition Principle figure of the present invention.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.
As shown in Figure 1, the Geostationary Satellite's provided by the invention based on MLE and Monte-Carlo Simulation, including such as
Lower step：
(1), criterion is terminated according to the lifetime of satellite, determines each subsystem units product that the lifetime of satellite can be caused to terminate；
The satellite operation on orbit service life refers generally to satellite since being entered the orbit transmitting, until operation on orbit is expendable because occurring
The duration that wearout failure causes satellite major function to be lost occurs for failure or end of lifetime.Judge whether the lifetime of satellite terminates
First it should be understood that functional performance of the satellite system during actual task, undergoing mission profile and environmental condition generally comprises
It is satellite orbital altitude, space environment, image quality requirement, orbit maneuver requirement, the orbit maneuver time, target location accuracy, flat
Platform gesture stability Capability Requirement, data transfer and storage capacity requirement, operation on orbit life requirements etc., tasks clear is successfully determined
Justice.Then, reselection judges the parameter whether lifetime of satellite terminates, these parameters should be understood that, quantify, and can reflect satellite comprehensively
Function, performance state, generally lose from load, orbit maneuver, gesture stability, power supply and distribution, data transfer and storage etc. refinement
Imitate criterion.
(2), each subsystem units product that the lifetime of satellite can be caused to terminate determined by step (1) is divided into random mistake
Imitate two class of product and wearout failure product；
The classification that GPS and NASA fails satellite is used for reference, satellite failure is divided into random failure and wearout failure by this step
Two major classes.Random failure refers mainly to the burst failure of electronic product in satellite；Wearout failure relates generally to satellite clock, the energy, electricity
Source power, lubrication, mechanism number of revolutions etc. show as gradually weakening the failure of characteristic within the lifetime of satellite phase.Will be in lifetime
The product for showing as random failure is divided into random failure product, and the product that wearout failure is shown as in lifetime is divided into
Wearout failure product.To not only there are random failure but also there are the unit of wearout failure, only considering random failure at this time, being classified as
Random failure product.
(3), the life model of random failure product is established, according to each unit crash rate of satellite random failure product and Qi Ke
By property logical relation, using monte carlo simulation methodology, the mean time to failure of satellite system random failure is tried to achieve
MTTF, is derived from satellite system random failure reliability function RAt random(t)；
The life model of the random failure product can select the reliability model of the product, random failure product can
Exponential distribution form can be used by property function R.I.e.：
R=e-λt
Wherein, R is reliability, and λ is crash rate, and t is the defined time；
Satellite electron class unit random failure distribution pattern can also use Weibull distribution to represent.
Satellite random failure can be characterized using MTTF indexs, specifically try to achieve satellite system using monte carlo simulation methodology
The mean time to failure MTTF processes of random failure are as shown in Figure 2：
(3.1), simulation parameter is initialized, the simulation parameter includes：Simulation times thresholding N, default emulation step number threshold value
K, analysis time t, analysis time step delta t, emulation step number i.In view of calculating the reason such as time and convergence rate, simulation times
Thresholding N generally may be set to 10000 times, and analysis time t is arranged to that satellite is in-orbit to require working time, analysis time step delta t
It is configured as needed, generally 1 day, January etc., emulation step number i is initialized as 0, when presetting emulation step number threshold k by analyzing
Between total duration determine.
(3.2), emulation step number i is added 1, renewal emulation step number i；
(3.3), it is 0 to initialize the simulation times n and Reliablility simulation number r met the requirements；
(3.4), simulation times n is added 1；
(3.5), random number is produced as random Reliability index by the use of the random function of MATLAB, produced according to random failure
The life model of product, calculates the failure reliability index corresponding out-of-service time of each random failure product unit, with reference to
Reliability logic relation between machine failure each unit of product, obtains the service life T of random failure products, enter step (3.6)；
Judged by the reliability logic relation between each unit of random failure product, obtain random failure product
Service life TsSpecific method be：
When the reliability logic relation between each unit of random failure product is parallel relationship, put down before choosing first-time fault
The greater of equal time MTTF, the service life T as random failure products；
When the reliability logic relation between each unit of random failure product is series relationship, put down before choosing first-time fault
The smaller of equal time MTTF, the service life T as random failure products；
When the reliability logic relation between each unit of random failure product is voting relation, put down before choosing first-time fault
The summation of equal time MTTF, the service life T as random failure products。
Product for taking redundant configuration strategy on satellite, in Reliablility simulation with logic criterion go description product it
Between redundancy relationship.If unit A and unit B is parallel redundancy, the criterion of MTTF is in emulation：MTTF's is larger in selection A or B
Person；If unit A and unit B is series relationship, the criterion of MTTF is in emulation：Select the smaller of MTTF in A or B.For table
Certainly relation, the criterion of MTTF is in emulation：Select the sum of A and BMTTF.
(3.6), the service life T of random failure product is comparedsThe index of aging T given with satellitemSize, if random lose
Imitate the service life T of productsThe index of aging T given more than or equal to satellitem, represent that random Reliability index at this time meets the system longevity
Index request is ordered, the reliability number r for meeting reliability requirement is added 1, reliability number r is updated, otherwise, as simulation times n
During not up to simulation times thresholding N, repeat step (3.4)~step (3.6), otherwise, enters step (3.7)；
(3.7), analysis time t corresponding satellite system reliability is calculatedAnalysis time t is according to simulation step length Δ
T is incremented by, will analysis time t plus Δ t with replacement analysis time t；
(3.8), emulation step number is added 1, renewal emulation step number i, compares emulation step number and be less than or equal to default emulation step number
Threshold k, then repeat step (3.2)~step (3.8), obtains the longevity of the random failure product corresponding to each simulation analysis time
Order Ts, otherwise, enter step (3.9)；
(3.9), each simulation analysis time t+k Δ t, the service life T of corresponding random failure products, make satellite system
System reliability curve, is fitted curve to obtain satellite system random failure function RAt random(t)。
Satellite system random failure function RAt random(t) form is：
Simulation data result includes satellite system stochastic life MTTF, when carrying out lifetime of satellite prediction calculating, can also incite somebody to action
It asks inverse to be converted into satellite crash rate level.
(4), the life model of wearout failure product is established, is closed according to each unit reliability logic of satellite wearout failure product
System and its wear-out life average and variance, using monte carlo simulation methodology, are calculated average and the side of satellite wear-out life
Difference estimation, is derived from satellite system wearout failure reliability function RConsume(t)；Satellite wearout failure product refers generally on satellite
Limited life item, usually there are specific wearout failure mechanism, on-orbit fault is generally rendered as consuming, drifts about, fatigue and moves back
Change etc., such as the decay of solar battery array output power, the limitation of accumulator cell charging and discharging cycle-index, electromechanical movable part component lubrication not
Wear out failure caused by foot etc..
The life model of wearout failure product uses normal distribution form.Specially：
Wherein：
μLoss- satellite wear-out life average；
σLoss- satellite wear-out life variance.
Specifically simulation process is：
(4.1), wearout failure product simulation parameter is initialized, the wearout failure product simulation parameter includes consume unit
Choose number, simulation times thresholding N2；Simulation times thresholding N2It generally may be set to 10000 times, can be set again according to convergence rate
It is fixed.
(4.2), the lifetime data of collection consume class unit, calculates first moment, second moment obtains wearout failure attrition
Service life average and variance, for there are the wearout failure product of redundancy backup, its wear-out life is calculated using Monte-Carlo Simulation
Average and variance；
It is different that the parameter in its service life is characterized due to different product, such as storage battery circulation cycle, mechanical movable part rotation time
Number etc. is, it is necessary to be converted into unified chronomere, easy to carry out life prediction, also needs to consider satellite consume class unit redundant configuration
Situation, such as flywheel, Gyro, storage battery group redundancy scheme.
If the unit also wants computing redundancy unit wear-out life mean μ and variances sigma there are redundancy backup；Redundancy is set
The unit of meter, wear-out life mean μ and variances sigma after backup are calculated according to type of backup and logical relation.For existing
The random failure product of redundancy backup, its wear-out life average and variance are calculated using Monte-Carlo Simulation.
(4.3), according to wearout failure attrition service life average and variance, the life model of wearout failure product is established,
Carry out N2Secondary Monte-Carlo Simulation calculates, and obtains the wearout failure time simulation value of each random failure product；
(4.4), according to the wearout failure time simulation result of wearout failure product, satellite average loss service life average is obtained
μLossAnd variances sigmaLossEstimation so that obtain satellite wearout failure average life span estimation, also therefore obtain wearout failure
Reliability function RConsume(t)。
The reliability function R of satellite wearout failureConsume(t) form is：
(5), satellite system random failure and wearout failure function are subjected to integration analysis, obtain the satellite task that is averaged and continue
Time MMD, in this, as the life prediction result of satellite system.
As shown in figure 4, the theoretical calculation formula of satellite mean mission duration time, MMDT MMD is：
R (t) --- mission reliability pattern function；
T --- truncated time.
The satellite mean mission duration time, MMDT MMD service lifes of the whole star of satellite of the present invention depend on the reliability curve of system, and
Satellite system reliability curve is by satellite random failure and the coefficient result of wearout failure.
Satellite mean mission duration time, MMDT MMD is：
1.5 times are required since service life extrapolation is limited solely to projected life, when asking for satellite average life span estimation, is cut
The tail time chooses 1.5 times of projected life requirement.
It is not described in detail in this specification and partly belongs to general knowledge well known to those skilled in the art.
Claims (8)
1. the Geostationary Satellite's based on MLE and Monte-Carlo Simulation, it is characterised in that include the following steps：
(1), criterion is terminated according to the lifetime of satellite, determines each subsystem units product that the lifetime of satellite can be caused to terminate；
(2), each subsystem units product that the lifetime of satellite terminates will can be caused to be divided into random failure production determined by step (1)
Two class of product and wearout failure product；
(3), the life model of random failure product is established, according to each unit crash rate of satellite random failure product and its reliability
Logical relation, using monte carlo simulation methodology, tries to achieve the mean time to failure MTTF of satellite system random failure, by
This obtains satellite system random failure reliability function RAt random(t)；
(4), establish the life model of wearout failure product, according to each unit reliability logic relation of satellite wearout failure product and
Its wear-out life average and variance, using monte carlo simulation methodology, the average and variance that satellite wear-out life is calculated are estimated
Meter, is derived from satellite system wearout failure reliability function RConsume(t)；
(5), satellite system random failure and wearout failure function are subjected to integration analysis, obtain satellite mean mission duration time, MMDT
MMD, in this, as the life prediction result of satellite system.
2. the Geostationary Satellite's according to claim 1 based on MLE and Monte-Carlo Simulation, it is characterised in that institute
The life model for stating random failure product is the reliability model of the product.
3. the Geostationary Satellite's according to claim 1 based on MLE and Monte-Carlo Simulation, it is characterised in that institute
State being implemented as step (3)：
(3.1), simulation parameter is initialized, the simulation parameter includes：Simulation times thresholding N, default emulation step number threshold k, divide
Analyse time t, analysis time step delta t, emulation step number i；
(3.2), emulation step number i is added 1, renewal emulation step number i；
(3.3), initialize simulation times n and meet that the reliability number r of reliability requirement is 0；
(3.4), simulation times n is added 1；
(3.5), random number is produced as random Reliability index by the use of the random function of MATLAB, according to random failure product
Life model, calculates the failure reliability index corresponding out-of-service time of each random failure product unit, is lost with reference to random
The reliability logic relation between each unit of product is imitated, obtains the service life T of random failure products, enter step (3.6)；
(3.6), the service life T of random failure product is comparedsThe index of aging T given with satellitemSize, if random failure produce
The service life T of productsThe index of aging T given more than or equal to satellitem, represent that random Reliability index at this time meets that lifetime of system refers to
Mark requires, and the reliability number r for meeting reliability requirement is added 1, reliability number r is updated, otherwise, when simulation times n does not reach
During to simulation times thresholding N, repeat step (3.4)~step (3.6), otherwise, enters step (37)；
(3.7), analysis time t corresponding satellite system reliability is calculatedAnalysis time t is passed according to simulation step length Δ t
Increase, will analysis time t plus Δ t with replacement analysis time t；
(3.8), emulation step number is added into 1, renewal emulation step number i, compares emulation step number and be less than or equal to default emulation step number threshold value
K, then repeat step (3.2)~step (3.8), obtains the service life of the random failure product corresponding to each simulation analysis time
Ts, otherwise, enter step (3.9)；
(3.9), each simulation analysis time t+k Δ t, the service life T of corresponding random failure products, it is reliable to make satellite system
Linearity curve, is fitted curve to obtain satellite system random failure function RAt random(t)。
4. the Geostationary Satellite's according to claim 3 based on MLE and Monte-Carlo Simulation, it is characterised in that step
Suddenly judged in (3.5) by the reliability logic relation between each unit of random failure product, obtain random failure product
Service life TsSpecific method be：
When the reliability logic relation between each unit of random failure product is parallel relationship, mean time before first-time fault is chosen
Between MTTF the greater, the service life T as random failure products；
When the reliability logic relation between each unit of random failure product is series relationship, mean time before first-time fault is chosen
Between MTTF smaller, the service life T as random failure products；
When the reliability logic relation between each unit of random failure product is voting relation, mean time before first-time fault is chosen
Between MTTF summation, the service life T as random failure products。
5. the Geostationary Satellite's according to claim 1 based on MLE and Monte-Carlo Simulation, it is characterised in that institute
The life model for stating wearout failure product uses normal distribution form, is specially：
Wherein：
μLoss- satellite wear-out life average；
σLoss- satellite wear-out life variance.
6. the Geostationary Satellite's according to claim 1 based on MLE and Monte-Carlo Simulation, it is characterised in that institute
State being implemented as step (4)：
(4.1), wearout failure product simulation parameter is initialized, the wearout failure product simulation parameter includes simulation times thresholding
N2；
(4.2), the lifetime data of collection consume class unit, calculates first moment, second moment obtains the wearout failure attrition service life
Average and variance, for there are the wearout failure product of redundancy backup, its wear-out life average is calculated using Monte-Carlo Simulation
And variance；
(4.3), according to wearout failure attrition service life average and variance, the life model of wearout failure product is established, carries out N2
Secondary Monte-Carlo Simulation calculates, and obtains the wearout failure time simulation value of each random failure product；
(4.4), according to the wearout failure time simulation result of wearout failure product, satellite average loss service life mean μ is obtainedLoss
And variances sigmaLossEstimation so that obtain satellite wearout failure average life span estimation, also therefore obtain the reliable of wearout failure
Property function RConsume(t)。
7. the Geostationary Satellite's according to claim 1 based on MLE and Monte-Carlo Simulation, it is characterised in that institute
The calculation formula for stating step (5) satellite mean mission duration time, MMDT MMD is：
In formula, T is truncated time.
8. the Geostationary Satellite's according to claim 6 based on MLE and Monte-Carlo Simulation, it is characterised in that institute
State truncated time T and choose 1.5 times of satellite design lifetime requirement.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711291915.2A CN108009366A (en) | 2017-12-08 | 2017-12-08 | Geostationary Satellite's based on MLE and Monte-Carlo Simulation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711291915.2A CN108009366A (en) | 2017-12-08 | 2017-12-08 | Geostationary Satellite's based on MLE and Monte-Carlo Simulation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108009366A true CN108009366A (en) | 2018-05-08 |
Family
ID=62057289
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711291915.2A Pending CN108009366A (en) | 2017-12-08 | 2017-12-08 | Geostationary Satellite's based on MLE and Monte-Carlo Simulation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108009366A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109614684A (en) * | 2018-12-04 | 2019-04-12 | 中国航天标准化研究所 | A kind of satellite electron method for predicting service life of product under more stress resultants |
| CN112257268A (en) * | 2020-10-23 | 2021-01-22 | 南京工业大学 | Reliability analysis method for hydraulic multi-way valve |
| CN112446138A (en) * | 2020-11-05 | 2021-03-05 | 航天东方红卫星有限公司 | Complex constellation system reliability modeling analysis method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070087756A1 (en) * | 2005-10-04 | 2007-04-19 | Hoffberg Steven M | Multifactorial optimization system and method |
| CN101169801A (en) * | 2007-08-18 | 2008-04-30 | 燕山大学 | Emulation method for spare parts life span distribution for influencing systematic reliability in afterward maintenance |
| US20140072837A1 (en) * | 2011-04-01 | 2014-03-13 | Sakti3, Inc. | Electric vehicle propulsion system and method utilizing solid-state rechargeable electrochemical cells |
| CN103810383A (en) * | 2014-01-27 | 2014-05-21 | 中国航天标准化研究所 | Method for identifying and analyzing single-point failure mode of launch vehicle |
| CN103954914A (en) * | 2014-05-16 | 2014-07-30 | 哈尔滨工业大学 | Lithium ion battery remaining life direct prediction method based on probability integration |
| CN106250616A (en) * | 2016-07-29 | 2016-12-21 | 北京空间飞行器总体设计部 | The aeronautical satellite Constellation availability appraisal procedure estimated based on dynamic reliability |
-
2017
- 2017-12-08 CN CN201711291915.2A patent/CN108009366A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070087756A1 (en) * | 2005-10-04 | 2007-04-19 | Hoffberg Steven M | Multifactorial optimization system and method |
| CN101169801A (en) * | 2007-08-18 | 2008-04-30 | 燕山大学 | Emulation method for spare parts life span distribution for influencing systematic reliability in afterward maintenance |
| US20140072837A1 (en) * | 2011-04-01 | 2014-03-13 | Sakti3, Inc. | Electric vehicle propulsion system and method utilizing solid-state rechargeable electrochemical cells |
| CN103810383A (en) * | 2014-01-27 | 2014-05-21 | 中国航天标准化研究所 | Method for identifying and analyzing single-point failure mode of launch vehicle |
| CN103954914A (en) * | 2014-05-16 | 2014-07-30 | 哈尔滨工业大学 | Lithium ion battery remaining life direct prediction method based on probability integration |
| CN106250616A (en) * | 2016-07-29 | 2016-12-21 | 北京空间飞行器总体设计部 | The aeronautical satellite Constellation availability appraisal procedure estimated based on dynamic reliability |
Non-Patent Citations (4)
| Title |
|---|
| 刘钦文等: "基于蒙特卡罗的系统任务可靠性分析方法", 《可靠性与环境适应性理论研究》 * |
| 吴雷等: "卫星寿命指标分配方法研究", 《航天器工程》 * |
| 徐子军等: "基于蒙特卡罗的石化设备寿命均值估计优化与应用", 《中国安全科学学报》 * |
| 龚佩佩等: "全球卫星导航系统性能标准及指标体系概述", 《第四届中国卫星导航学术年会电子文集》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109614684A (en) * | 2018-12-04 | 2019-04-12 | 中国航天标准化研究所 | A kind of satellite electron method for predicting service life of product under more stress resultants |
| CN109614684B (en) * | 2018-12-04 | 2022-10-28 | 中国航天标准化研究所 | Method for predicting service life of satellite electronic product under multi-stress synthesis |
| CN112257268A (en) * | 2020-10-23 | 2021-01-22 | 南京工业大学 | Reliability analysis method for hydraulic multi-way valve |
| CN112257268B (en) * | 2020-10-23 | 2024-05-24 | 南京工业大学 | Reliability analysis method for hydraulic multi-way valve |
| CN112446138A (en) * | 2020-11-05 | 2021-03-05 | 航天东方红卫星有限公司 | Complex constellation system reliability modeling analysis method |
| CN112446138B (en) * | 2020-11-05 | 2023-12-29 | 航天东方红卫星有限公司 | Reliability modeling analysis method for complex constellation system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| El Mejdoubi et al. | Remaining useful life prognosis of supercapacitors under temperature and voltage aging conditions | |
| Wenzl et al. | Life prediction of batteries for selecting the technically most suitable and cost effective battery | |
| CN111680848A (en) | Battery life prediction method based on prediction model fusion and storage medium | |
| CN108009366A (en) | Geostationary Satellite's based on MLE and Monte-Carlo Simulation | |
| US20220057451A1 (en) | System and method for estimation of battery state and health | |
| Vu et al. | A stationary grouping maintenance strategy using mean residual life and the birnbaum importance measure for complex structures | |
| Kim et al. | A novel prognostics approach using shifting kernel particle filter of Li-ion batteries under state changes | |
| Jin | Reliability engineering and services | |
| CN102798823A (en) | Gaussian process regression-based method for predicting state of health (SOH) of lithium batteries | |
| Jouin et al. | Prognostics of proton exchange membrane fuel cell stack in a particle filtering framework including characterization disturbances and voltage recovery | |
| CN104156791A (en) | Lithium ion battery residual life predicting method based on LS-SVM probability ensemble learning | |
| JP6117349B2 (en) | Simple calculation model generation method and component consumption life prediction method | |
| Levitin et al. | Reliability of multi-state systems with free access to repairable standby elements | |
| US20210270904A1 (en) | State estimation method and state estimation apparatus | |
| Zhao et al. | Online estimation of satellite lithium-ion battery capacity based on approximate belief rule base and hidden Markov model | |
| KR102632198B1 (en) | Apparatus and method for predicting and managing force evolution over cycle in battery packs | |
| Peikun et al. | Research of the relationship between Li-ion battery charge performance and SOH based on MIGA-Gpr method | |
| Li et al. | An adaptive threshold method for multi-faults diagnosis of lithium-ion batteries based on electro-thermal model | |
| Goebel et al. | Prognostics applied to electric propulsion UAV | |
| Wu et al. | Online prognostication of remaining useful life for random discharge lithium-ion batteries using a gamma process model | |
| Mamun et al. | Physics-based simulation of the impact of demand response on lead-acid emergency power availability in a datacenter | |
| Levitin et al. | 1-out-of-n standby systems with storages and activation moment-dependent component operation time limit | |
| Zhao et al. | Electric Vehicle Battery End-Of-Use Recovery Management: Degradation Prediction and Decision Making | |
| CN110427689A (en) | Combustion engine unit group of planes monitoring, diagnosing method based on new information technology | |
| CN108563142B (en) | Spacecraft ACS task life prediction method based on SHA |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Wang Jinjing Inventor after: Qi Yalin Inventor after: Zhang Qingjun Inventor after: Tang Zhihua Inventor after: Wang Tao Inventor after: Wang Jianjun Inventor after: Zhou Bo Inventor after: Xue En Inventor after: Jiao Shuyuan Inventor before: Wang Jinjing Inventor before: Qi Yalin Inventor before: Zhang Qingjun Inventor before: Tang Zhihua Inventor before: Wang Tao Inventor before: Zhou Bo Inventor before: Xue En Inventor before: Jiao Shuyuan |
|
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180508 |